Payment Processing - Stripe received $685 million in funding last year and is valued at over $20B

Personal Finance - This is a broad market, but we're seeing the "average joe" empowered to make smarter personal finance decisions with companies like Robinhood, Acorns, and Credit Karma. Credit Karma raised $869M and is valued at approximately $4B

Blockchain - While crypto is currently out of favor for many, blockchain is still an exciting prospect. Opendoor is bypassing real estate agents and brokers to disrupt the housing market. Receiving over $1B in funding last year, it is currently valued at $3.7B.

These are the sectors getting the most Fintech funding now (with payment processing the clear leader).

In my opinion, real disruption is brewing in the personal finance & Wall Street sectors ... and funding is ramping up there as we speak.

This Fintech sector reminds me of biotech. Solutions have to be more mature and fully-tested before unleashing them on the public. But winners will Win!

Wall Street is one of the smaller sections in this infographic. Nonetheless, I think you'll be reading a lot more about this space soon. Here are some of the companies worth noting.

Symphony does secure communications for companies like JP Morgan and Goldman Sachs. It helps with internal communication, and client-centric communication, and has various bots and apps to decrease decision time.

Not seen on these lists are all the smaller companies at the beginning of their innovation cycle, and the companies doing their best to stay under the radar. More competitors and more opportunities mean more reason to keep your cards close to your chest.

The funding cycle is always two steps behind the innovation cycle, which means ideas we've been hearing about from the likes of Gartner will be seeing increased publicity soon.

NASA's Mars Opportunity Rover has officially lost contact with earth after a fierce dust storm. Its last message was "My battery is getting low and it's getting dark." The rover's original mission was scheduled for 90 days ... it lasted 14 years.

This week, I spoke on stage at IBM's Think! 2019 conference in San Francisco. Our topic was "Gaining a Financial Edge through Fast Data and Analytics." I think it went well (both the presentation and our exposure to lots of useful technology, talent, and opportunities).

Moscone Center was filled with people clamoring for information about artificial intelligence, big data, and flexible computing (think of it as being able to do what you want locally, in the cloud, or with a hybrid cloud ... or through hardware or software). That means solutions are becoming independent of how they are produced, delivered, or consumed.

February 10, 2019

One thing that Deep Learning excels in is analyzing pictures & videos, and creating facsimiles or combining styles. If you want to create art with deep learning look no further than the Deep Dream Generator or deepart.io which use Convolutional Neural Networks to combine your photo with an art style (if you want to do it on your phone another cool tool to check out is Prisma).

Deepfake is it's exactly what it sounds like ... the use "Deep Learning" to "Fake" a recording. For example, a machine learning technique called a Generative Adversarial Network can be used to superimpose images onto a source video. That is how they made this fun (and disturbing) Deepfake of Jennifer Lawrence and Steve Buscemi.

While this is a fun example, Deepfakes create very real concerns. They're often used for more "nefarious" purposes (e.g., to create fake celebrity or revenge porn and to otherwise make important figures say things they never said). It's likely you've seen videos of Trump or Obama created with this technology. But it is easy to imagine someone faking evidence used at trial, trying to influence business transactions, or using this to support or slander causes in the media.

Back to Reality

Rationally, we understand that football and the stock market have nothing in common. And we probably intuitively understand that correlation ≠ causation. Yet, we crave order, and look for signs that make markets seem a little bit more predictable.

The problem with randomness is that it can appear meaningful.

Wall Street is, unfortunately, inundated with theories that attempt to predict the performance of the stock market and the economy. The only difference between this and other theories is that we openly recognize the ridiculousness of this indicator.

More people than you would hope, or guess, attempt to forecast the market based on gut, ancient wisdom, and prayers.

While hope and prayer are good things ... they aren’t good trading strategies..

As goofy as it sounds, some of these "far-fetched" theories perform better than professional money managers with immense capital, research teams, and decades of experience.

I have a thought experiment I often ask people that come into my office.

What percentage of active managers beat the S&P 500 any given year?

... Now, what percentage beat the S&P 500 over 15 years?

The answer is about 5% (and that's in a predominantly bull market). That's significantly worse than chance. That means something they're doing is hurting, not helping.